Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations1392
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory680.3 KiB
Average record size in memory500.5 B

Variable types

DateTime2
Numeric21
Text1
Boolean2
Categorical4

Alerts

Armor Cache is highly overall correlated with Medium Arms Cache and 12 other fieldsHigh correlation
Medium Arms Cache is highly overall correlated with Armor Cache and 11 other fieldsHigh correlation
Melee Cache is highly overall correlated with Armor Cache and 10 other fieldsHigh correlation
Small Arms Cache is highly overall correlated with total_cache_valueHigh correlation
attacks is highly overall correlated with Armor Cache and 7 other fieldsHigh correlation
duration_hours is highly overall correlated with duration_minutesHigh correlation
duration_minutes is highly overall correlated with duration_hoursHigh correlation
faction_id is highly overall correlated with Armor Cache and 11 other fieldsHigh correlation
faction_total_wins is highly overall correlated with faction_win_percentage and 1 other fieldsHigh correlation
faction_win_percentage is highly overall correlated with faction_total_wins and 2 other fieldsHigh correlation
member_count is highly overall correlated with Armor Cache and 10 other fieldsHigh correlation
participating_member_count is highly overall correlated with Armor Cache and 11 other fieldsHigh correlation
rank_after is highly overall correlated with rank_before and 3 other fieldsHigh correlation
rank_before is highly overall correlated with rank_after and 2 other fieldsHigh correlation
rank_number_after is highly overall correlated with Armor Cache and 13 other fieldsHigh correlation
rank_number_before is highly overall correlated with Armor Cache and 13 other fieldsHigh correlation
reward_points is highly overall correlated with Armor Cache and 7 other fieldsHigh correlation
reward_respect is highly overall correlated with Armor Cache and 12 other fieldsHigh correlation
score is highly overall correlated with Armor Cache and 13 other fieldsHigh correlation
score_pct_of_war_total is highly overall correlated with attacks and 3 other fieldsHigh correlation
total_cache_value is highly overall correlated with Armor Cache and 13 other fieldsHigh correlation
total_war_score is highly overall correlated with Armor Cache and 12 other fieldsHigh correlation
won is highly overall correlated with faction_total_wins and 3 other fieldsHigh correlation
Heavy Arms Cache is highly imbalanced (68.2%) Imbalance
forfeit is highly imbalanced (96.0%) Imbalance
reward_points has 656 (47.1%) zeros Zeros
Small Arms Cache has 561 (40.3%) zeros Zeros
Armor Cache has 584 (42.0%) zeros Zeros
Melee Cache has 543 (39.0%) zeros Zeros
Medium Arms Cache has 546 (39.2%) zeros Zeros
faction_win_percentage has 374 (26.9%) zeros Zeros
rank_number_before has 77 (5.5%) zeros Zeros
rank_number_after has 45 (3.2%) zeros Zeros
total_cache_value has 74 (5.3%) zeros Zeros

Reproduction

Analysis started2025-07-22 17:14:47.948338
Analysis finished2025-07-22 17:15:44.913837
Duration56.97 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct541
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
Minimum2024-09-27 02:00:00
Maximum2025-07-19 18:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-22T12:15:45.000368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:45.180960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct696
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
Minimum2024-09-29 14:34:00
Maximum2025-07-22 01:01:04
Invalid dates0
Invalid dates (%)0.0%
2025-07-22T12:15:45.352035image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:45.527891image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

duration_hours
Real number (ℝ)

High correlation 

Distinct548
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.459325
Minimum3.13
Maximum116.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:45.691844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3.13
5-th percentile11.2885
Q129.9325
median50.2
Q371.585
95-th percentile102.02
Maximum116.02
Range112.89
Interquartile range (IQR)41.6525

Descriptive statistics

Standard deviation27.936789
Coefficient of variation (CV)0.53254192
Kurtosis-0.83450427
Mean52.459325
Median Absolute Deviation (MAD)20.735
Skewness0.2794112
Sum73023.38
Variance780.4642
MonotonicityNot monotonic
2025-07-22T12:15:45.892401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.02 14
 
1.0%
55.02 14
 
1.0%
82.02 14
 
1.0%
91.02 12
 
0.9%
95.02 12
 
0.9%
78.02 12
 
0.9%
90.02 10
 
0.7%
56.02 10
 
0.7%
49.02 10
 
0.7%
65.02 10
 
0.7%
Other values (538) 1274
91.5%
ValueCountFrequency (%)
3.13 2
0.1%
4.33 2
0.1%
4.4 2
0.1%
4.71 2
0.1%
4.78 2
0.1%
4.82 2
0.1%
5.11 2
0.1%
6.02 2
0.1%
6.34 2
0.1%
6.52 4
0.3%
ValueCountFrequency (%)
116.02 2
 
0.1%
115.61 2
 
0.1%
115.02 2
 
0.1%
113.02 2
 
0.1%
111.1 2
 
0.1%
111.02 2
 
0.1%
110.55 2
 
0.1%
110.02 2
 
0.1%
109.02 6
0.4%
108.13 2
 
0.1%

duration_minutes
Real number (ℝ)

High correlation 

Distinct536
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3147.4957
Minimum188
Maximum6961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:46.070993image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum188
5-th percentile677.2
Q11796.25
median3012
Q34294.75
95-th percentile6121
Maximum6961
Range6773
Interquartile range (IQR)2498.5

Descriptive statistics

Standard deviation1676.1469
Coefficient of variation (CV)0.53253349
Kurtosis-0.83447171
Mean3147.4957
Median Absolute Deviation (MAD)1244
Skewness0.27943796
Sum4381314
Variance2809468.3
MonotonicityNot monotonic
2025-07-22T12:15:46.266921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3421 14
 
1.0%
4921 14
 
1.0%
3301 14
 
1.0%
5701 12
 
0.9%
5461 12
 
0.9%
4681 12
 
0.9%
3901 10
 
0.7%
3661 10
 
0.7%
5401 10
 
0.7%
3361 10
 
0.7%
Other values (526) 1274
91.5%
ValueCountFrequency (%)
188 2
0.1%
260 2
0.1%
264 2
0.1%
283 2
0.1%
287 2
0.1%
289 2
0.1%
306 2
0.1%
361 2
0.1%
381 2
0.1%
391 4
0.3%
ValueCountFrequency (%)
6961 2
 
0.1%
6937 2
 
0.1%
6901 2
 
0.1%
6781 2
 
0.1%
6666 2
 
0.1%
6661 2
 
0.1%
6633 2
 
0.1%
6601 2
 
0.1%
6541 6
0.4%
6488 2
 
0.1%

war_id
Real number (ℝ)

Distinct696
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23185.935
Minimum18131
Maximum28089
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:46.462735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum18131
5-th percentile18677.75
Q120831.75
median23108.5
Q325717.5
95-th percentile27594.35
Maximum28089
Range9958
Interquartile range (IQR)4885.75

Descriptive statistics

Standard deviation2866.459
Coefficient of variation (CV)0.12362922
Kurtosis-1.2094197
Mean23185.935
Median Absolute Deviation (MAD)2441.5
Skewness-0.010439622
Sum32274822
Variance8216587.2
MonotonicityNot monotonic
2025-07-22T12:15:46.667715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20149 2
 
0.1%
28013 2
 
0.1%
19075 2
 
0.1%
21794 2
 
0.1%
27260 2
 
0.1%
19276 2
 
0.1%
25942 2
 
0.1%
24267 2
 
0.1%
21792 2
 
0.1%
26933 2
 
0.1%
Other values (686) 1372
98.6%
ValueCountFrequency (%)
18131 2
0.1%
18134 2
0.1%
18155 2
0.1%
18178 2
0.1%
18189 2
0.1%
18214 2
0.1%
18215 2
0.1%
18242 2
0.1%
18247 2
0.1%
18248 2
0.1%
ValueCountFrequency (%)
28089 2
0.1%
28084 2
0.1%
28068 2
0.1%
28058 2
0.1%
28052 2
0.1%
28051 2
0.1%
28015 2
0.1%
28013 2
0.1%
28006 2
0.1%
27981 2
0.1%

faction_id
Real number (ℝ)

High correlation 

Distinct822
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39000.745
Minimum89
Maximum54339
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:46.846705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile8336
Q123952
median48680
Q351629.75
95-th percentile53462.8
Maximum54339
Range54250
Interquartile range (IQR)27677.75

Descriptive statistics

Standard deviation16695.502
Coefficient of variation (CV)0.42808161
Kurtosis-0.73004079
Mean39000.745
Median Absolute Deviation (MAD)4290
Skewness-0.94144622
Sum54289037
Variance2.7873977 × 108
MonotonicityNot monotonic
2025-07-22T12:15:47.055290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
937 7
 
0.5%
23952 6
 
0.4%
21368 6
 
0.4%
8677 6
 
0.4%
49247 6
 
0.4%
7835 6
 
0.4%
10184 5
 
0.4%
30584 5
 
0.4%
51056 5
 
0.4%
14821 5
 
0.4%
Other values (812) 1335
95.9%
ValueCountFrequency (%)
89 1
 
0.1%
230 1
 
0.1%
231 3
0.2%
366 3
0.2%
478 1
 
0.1%
525 1
 
0.1%
937 7
0.5%
1117 1
 
0.1%
1251 1
 
0.1%
2095 1
 
0.1%
ValueCountFrequency (%)
54339 1
0.1%
54260 1
0.1%
54250 1
0.1%
54249 1
0.1%
54245 1
0.1%
54226 1
0.1%
54214 2
0.1%
54212 1
0.1%
54200 1
0.1%
54197 1
0.1%
Distinct822
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Memory size86.2 KiB
2025-07-22T12:15:47.562173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length25
Median length19
Mean length14.047414
Min length3

Characters and Unicode

Total characters19554
Distinct characters86
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique480 ?
Unique (%)34.5%

Sample

1st rowNub Navy
2nd rowValor
3rd rowColombo Crime Family
4th rowToxic Spawn
5th rowThe Swarm
ValueCountFrequency (%)
the 218
 
7.0%
of 90
 
2.9%
73
 
2.3%
monarch 37
 
1.2%
torn 21
 
0.7%
hq 21
 
0.7%
family 19
 
0.6%
x 18
 
0.6%
legion 16
 
0.5%
black 15
 
0.5%
Other values (1148) 2587
83.0%
2025-07-22T12:15:48.060144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1738
 
8.9%
e 1625
 
8.3%
a 1256
 
6.4%
o 1089
 
5.6%
r 1085
 
5.5%
i 1056
 
5.4%
n 927
 
4.7%
s 906
 
4.6%
t 881
 
4.5%
l 745
 
3.8%
Other values (76) 8246
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19554
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1738
 
8.9%
e 1625
 
8.3%
a 1256
 
6.4%
o 1089
 
5.6%
r 1085
 
5.5%
i 1056
 
5.4%
n 927
 
4.7%
s 906
 
4.6%
t 881
 
4.5%
l 745
 
3.8%
Other values (76) 8246
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19554
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1738
 
8.9%
e 1625
 
8.3%
a 1256
 
6.4%
o 1089
 
5.6%
r 1085
 
5.5%
i 1056
 
5.4%
n 927
 
4.7%
s 906
 
4.6%
t 881
 
4.5%
l 745
 
3.8%
Other values (76) 8246
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19554
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1738
 
8.9%
e 1625
 
8.3%
a 1256
 
6.4%
o 1089
 
5.6%
r 1085
 
5.5%
i 1056
 
5.4%
n 927
 
4.7%
s 906
 
4.6%
t 881
 
4.5%
l 745
 
3.8%
Other values (76) 8246
42.2%

won
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
False
696 
True
696 
ValueCountFrequency (%)
False 696
50.0%
True 696
50.0%
2025-07-22T12:15:48.193352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

score
Real number (ℝ)

High correlation 

Distinct1271
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4726.7313
Minimum0
Maximum58790
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:48.327573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile355.95
Q11596.25
median2771.5
Q35300.25
95-th percentile16098.6
Maximum58790
Range58790
Interquartile range (IQR)3704

Descriptive statistics

Standard deviation5812.6088
Coefficient of variation (CV)1.2297312
Kurtosis20.114397
Mean4726.7313
Median Absolute Deviation (MAD)1576.5
Skewness3.5994007
Sum6579610
Variance33786421
MonotonicityNot monotonic
2025-07-22T12:15:48.493855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2001 3
 
0.2%
2120 3
 
0.2%
2271 3
 
0.2%
673 3
 
0.2%
1953 3
 
0.2%
1281 3
 
0.2%
503 3
 
0.2%
2509 3
 
0.2%
2836 3
 
0.2%
2168 3
 
0.2%
Other values (1261) 1362
97.8%
ValueCountFrequency (%)
0 2
0.1%
6 1
0.1%
9 2
0.1%
10 1
0.1%
23 1
0.1%
26 1
0.1%
29 1
0.1%
30 1
0.1%
41 1
0.1%
51 1
0.1%
ValueCountFrequency (%)
58790 1
0.1%
53715 1
0.1%
53461 1
0.1%
52803 1
0.1%
39479 1
0.1%
37844 1
0.1%
37498 1
0.1%
33062 1
0.1%
32352 1
0.1%
31864 1
0.1%

total_war_score
Real number (ℝ)

High correlation 

Distinct675
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9453.4626
Minimum933
Maximum112251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:48.806169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum933
5-th percentile2238
Q13759
median6246.5
Q311614
95-th percentile24519.35
Maximum112251
Range111318
Interquartile range (IQR)7855

Descriptive statistics

Standard deviation9984.539
Coefficient of variation (CV)1.056178
Kurtosis31.638456
Mean9453.4626
Median Absolute Deviation (MAD)3096.5
Skewness4.4243467
Sum13159220
Variance99691019
MonotonicityNot monotonic
2025-07-22T12:15:48.959219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2552 4
 
0.3%
6204 4
 
0.3%
3962 4
 
0.3%
2813 4
 
0.3%
5780 4
 
0.3%
3347 4
 
0.3%
3693 4
 
0.3%
2926 4
 
0.3%
6353 4
 
0.3%
9963 4
 
0.3%
Other values (665) 1352
97.1%
ValueCountFrequency (%)
933 2
0.1%
1479 2
0.1%
1634 2
0.1%
1676 2
0.1%
1754 2
0.1%
1755 2
0.1%
1756 2
0.1%
1761 2
0.1%
1784 2
0.1%
1791 2
0.1%
ValueCountFrequency (%)
112251 2
0.1%
93194 2
0.1%
90301 2
0.1%
69708 2
0.1%
50595 2
0.1%
49518 2
0.1%
49242 2
0.1%
46998 2
0.1%
44612 2
0.1%
43604 2
0.1%

attacks
Real number (ℝ)

High correlation 

Distinct873
Distinct (%)62.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean636.05316
Minimum0
Maximum7105
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:49.106748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75.1
Q1265
median486
Q3789.5
95-th percentile1763.8
Maximum7105
Range7105
Interquartile range (IQR)524.5

Descriptive statistics

Standard deviation600.20185
Coefficient of variation (CV)0.94363473
Kurtosis22.696512
Mean636.05316
Median Absolute Deviation (MAD)249
Skewness3.3790789
Sum885386
Variance360242.26
MonotonicityNot monotonic
2025-07-22T12:15:49.244953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
731 6
 
0.4%
600 6
 
0.4%
420 6
 
0.4%
92 5
 
0.4%
407 5
 
0.4%
519 5
 
0.4%
237 5
 
0.4%
269 5
 
0.4%
180 4
 
0.3%
370 4
 
0.3%
Other values (863) 1341
96.3%
ValueCountFrequency (%)
0 2
0.1%
1 3
0.2%
2 1
 
0.1%
5 2
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
ValueCountFrequency (%)
7105 1
0.1%
6700 1
0.1%
5444 1
0.1%
4608 1
0.1%
3549 1
0.1%
3309 1
0.1%
3254 1
0.1%
3228 1
0.1%
3082 1
0.1%
2873 1
0.1%

rank_before
Categorical

High correlation 

Distinct21
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Bronze I
117 
Bronze
114 
Platinum I
95 
Silver I
95 
Platinum II
92 
Other values (16)
879 

Length

Max length12
Median length10
Mean length8.4942529
Min length4

Characters and Unicode

Total characters11824
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiamond I
2nd rowDiamond I
3rd rowSilver I
4th rowSilver I
5th rowGold II

Common Values

ValueCountFrequency (%)
Bronze I 117
 
8.4%
Bronze 114
 
8.2%
Platinum I 95
 
6.8%
Silver I 95
 
6.8%
Platinum II 92
 
6.6%
Platinum III 79
 
5.7%
Unranked 77
 
5.5%
Bronze II 73
 
5.2%
Gold I 69
 
5.0%
Silver II 68
 
4.9%
Other values (11) 513
36.9%

Length

2025-07-22T12:15:49.440755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i 417
17.2%
bronze 371
15.3%
platinum 317
13.1%
ii 313
12.9%
iii 302
12.5%
silver 270
11.1%
gold 218
9.0%
diamond 139
 
5.7%
unranked 77
 
3.2%

Most occurring characters

ValueCountFrequency (%)
I 1949
16.5%
1032
 
8.7%
n 981
 
8.3%
l 805
 
6.8%
o 728
 
6.2%
i 726
 
6.1%
r 718
 
6.1%
e 718
 
6.1%
a 533
 
4.5%
m 456
 
3.9%
Other values (12) 3178
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11824
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1949
16.5%
1032
 
8.7%
n 981
 
8.3%
l 805
 
6.8%
o 728
 
6.2%
i 726
 
6.1%
r 718
 
6.1%
e 718
 
6.1%
a 533
 
4.5%
m 456
 
3.9%
Other values (12) 3178
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11824
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1949
16.5%
1032
 
8.7%
n 981
 
8.3%
l 805
 
6.8%
o 728
 
6.2%
i 726
 
6.1%
r 718
 
6.1%
e 718
 
6.1%
a 533
 
4.5%
m 456
 
3.9%
Other values (12) 3178
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11824
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1949
16.5%
1032
 
8.7%
n 981
 
8.3%
l 805
 
6.8%
o 728
 
6.2%
i 726
 
6.1%
r 718
 
6.1%
e 718
 
6.1%
a 533
 
4.5%
m 456
 
3.9%
Other values (12) 3178
26.9%

rank_after
Categorical

High correlation 

Distinct21
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Platinum I
112 
Bronze
111 
Bronze I
111 
Bronze II
100 
Silver I
92 
Other values (16)
866 

Length

Max length12
Median length10
Mean length8.5387931
Min length4

Characters and Unicode

Total characters11886
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiamond
2nd rowDiamond II
3rd rowSilver
4th rowSilver II
5th rowGold I

Common Values

ValueCountFrequency (%)
Platinum I 112
 
8.0%
Bronze 111
 
8.0%
Bronze I 111
 
8.0%
Bronze II 100
 
7.2%
Silver I 92
 
6.6%
Gold I 88
 
6.3%
Platinum III 87
 
6.2%
Platinum II 74
 
5.3%
Silver II 70
 
5.0%
Silver III 65
 
4.7%
Other values (11) 482
34.6%

Length

2025-07-22T12:15:49.605491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i 440
17.8%
bronze 385
15.6%
ii 348
14.1%
platinum 323
13.1%
iii 295
11.9%
silver 279
11.3%
gold 222
9.0%
diamond 138
 
5.6%
unranked 45
 
1.8%

Most occurring characters

ValueCountFrequency (%)
I 2021
17.0%
1083
 
9.1%
n 936
 
7.9%
l 824
 
6.9%
o 745
 
6.3%
i 740
 
6.2%
e 709
 
6.0%
r 709
 
6.0%
a 506
 
4.3%
m 461
 
3.9%
Other values (12) 3152
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 2021
17.0%
1083
 
9.1%
n 936
 
7.9%
l 824
 
6.9%
o 745
 
6.3%
i 740
 
6.2%
e 709
 
6.0%
r 709
 
6.0%
a 506
 
4.3%
m 461
 
3.9%
Other values (12) 3152
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 2021
17.0%
1083
 
9.1%
n 936
 
7.9%
l 824
 
6.9%
o 745
 
6.3%
i 740
 
6.2%
e 709
 
6.0%
r 709
 
6.0%
a 506
 
4.3%
m 461
 
3.9%
Other values (12) 3152
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 2021
17.0%
1083
 
9.1%
n 936
 
7.9%
l 824
 
6.9%
o 745
 
6.3%
i 740
 
6.2%
e 709
 
6.0%
r 709
 
6.0%
a 506
 
4.3%
m 461
 
3.9%
Other values (12) 3152
26.5%

member_count
Real number (ℝ)

High correlation 

Distinct91
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.926006
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:49.768912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q115
median24
Q355.25
95-th percentile98
Maximum100
Range90
Interquartile range (IQR)40.25

Descriptive statistics

Standard deviation29.548451
Coefficient of variation (CV)0.77910792
Kurtosis-0.47678546
Mean37.926006
Median Absolute Deviation (MAD)11
Skewness1.0092196
Sum52793
Variance873.11098
MonotonicityNot monotonic
2025-07-22T12:15:49.935720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 96
 
6.9%
13 87
 
6.2%
14 75
 
5.4%
11 64
 
4.6%
12 58
 
4.2%
20 48
 
3.4%
16 46
 
3.3%
18 45
 
3.2%
19 42
 
3.0%
17 41
 
2.9%
Other values (81) 790
56.8%
ValueCountFrequency (%)
10 22
 
1.6%
11 64
4.6%
12 58
4.2%
13 87
6.2%
14 75
5.4%
15 96
6.9%
16 46
3.3%
17 41
2.9%
18 45
3.2%
19 42
3.0%
ValueCountFrequency (%)
100 32
2.3%
99 31
2.2%
98 20
1.4%
97 16
1.1%
96 18
1.3%
95 4
 
0.3%
94 9
 
0.6%
93 14
1.0%
92 6
 
0.4%
91 11
 
0.8%

participating_member_count
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.490661
Minimum0
Maximum95
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:50.092375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median15
Q330
95-th percentile70
Maximum95
Range95
Interquartile range (IQR)20

Descriptive statistics

Standard deviation20.050007
Coefficient of variation (CV)0.853531
Kurtosis1.8513037
Mean23.490661
Median Absolute Deviation (MAD)7
Skewness1.5877124
Sum32699
Variance402.00279
MonotonicityNot monotonic
2025-07-22T12:15:50.252426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 90
 
6.5%
10 83
 
6.0%
9 79
 
5.7%
13 69
 
5.0%
12 64
 
4.6%
14 61
 
4.4%
15 58
 
4.2%
8 56
 
4.0%
7 48
 
3.4%
6 42
 
3.0%
Other values (85) 742
53.3%
ValueCountFrequency (%)
0 2
 
0.1%
1 5
 
0.4%
2 7
 
0.5%
3 15
 
1.1%
4 18
 
1.3%
5 21
 
1.5%
6 42
3.0%
7 48
3.4%
8 56
4.0%
9 79
5.7%
ValueCountFrequency (%)
95 1
 
0.1%
94 1
 
0.1%
93 1
 
0.1%
92 4
0.3%
91 4
0.3%
90 3
0.2%
89 1
 
0.1%
88 5
0.4%
87 1
 
0.1%
85 3
0.2%

reward_respect
Real number (ℝ)

High correlation 

Distinct1233
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5111.199
Minimum0
Maximum33948
Zeros3
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:50.405343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile344.65
Q11390.75
median2516
Q35965
95-th percentile19790.95
Maximum33948
Range33948
Interquartile range (IQR)4574.25

Descriptive statistics

Standard deviation6240.1933
Coefficient of variation (CV)1.2208864
Kurtosis4.4711059
Mean5111.199
Median Absolute Deviation (MAD)1596
Skewness2.13886
Sum7114789
Variance38940012
MonotonicityNot monotonic
2025-07-22T12:15:50.585243image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1568 4
 
0.3%
1496 4
 
0.3%
1208 3
 
0.2%
1619 3
 
0.2%
1688 3
 
0.2%
1680 3
 
0.2%
1429 3
 
0.2%
1042 3
 
0.2%
823 3
 
0.2%
0 3
 
0.2%
Other values (1223) 1360
97.7%
ValueCountFrequency (%)
0 3
0.2%
3 1
 
0.1%
5 1
 
0.1%
9 1
 
0.1%
17 1
 
0.1%
18 1
 
0.1%
29 1
 
0.1%
32 1
 
0.1%
35 1
 
0.1%
40 1
 
0.1%
ValueCountFrequency (%)
33948 1
0.1%
33313 1
0.1%
32833 1
0.1%
32404 1
0.1%
31954 1
0.1%
31417 1
0.1%
30871 1
0.1%
30569 1
0.1%
30098 1
0.1%
29835 1
0.1%

reward_points
Real number (ℝ)

High correlation  Zeros 

Distinct671
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.8211
Minimum0
Maximum11203
Zeros656
Zeros (%)47.1%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:50.747781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median218.5
Q31388.25
95-th percentile5554.8
Maximum11203
Range11203
Interquartile range (IQR)1388.25

Descriptive statistics

Standard deviation1964.6854
Coefficient of variation (CV)1.6939555
Kurtosis6.0358815
Mean1159.8211
Median Absolute Deviation (MAD)218.5
Skewness2.4071128
Sum1614471
Variance3859988.6
MonotonicityNot monotonic
2025-07-22T12:15:50.897850image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 656
47.1%
596 3
 
0.2%
821 3
 
0.2%
715 3
 
0.2%
757 3
 
0.2%
813 3
 
0.2%
802 3
 
0.2%
589 3
 
0.2%
614 3
 
0.2%
534 3
 
0.2%
Other values (661) 709
50.9%
ValueCountFrequency (%)
0 656
47.1%
3 1
 
0.1%
6 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
15 1
 
0.1%
29 1
 
0.1%
32 1
 
0.1%
43 1
 
0.1%
48 1
 
0.1%
ValueCountFrequency (%)
11203 1
0.1%
10993 1
0.1%
10835 1
0.1%
10693 1
0.1%
10368 1
0.1%
10187 1
0.1%
9932 1
0.1%
9815 1
0.1%
9694 1
0.1%
9637 1
0.1%

Small Arms Cache
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0280172
Minimum0
Maximum10
Zeros561
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:51.030383image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2092108
Coefficient of variation (CV)1.1762554
Kurtosis5.3780023
Mean1.0280172
Median Absolute Deviation (MAD)1
Skewness1.8225644
Sum1431
Variance1.4621907
MonotonicityNot monotonic
2025-07-22T12:15:51.140436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 561
40.3%
1 482
34.6%
2 204
 
14.7%
3 83
 
6.0%
4 37
 
2.7%
5 15
 
1.1%
6 6
 
0.4%
8 2
 
0.1%
10 1
 
0.1%
7 1
 
0.1%
ValueCountFrequency (%)
0 561
40.3%
1 482
34.6%
2 204
 
14.7%
3 83
 
6.0%
4 37
 
2.7%
5 15
 
1.1%
6 6
 
0.4%
7 1
 
0.1%
8 2
 
0.1%
10 1
 
0.1%
ValueCountFrequency (%)
10 1
 
0.1%
8 2
 
0.1%
7 1
 
0.1%
6 6
 
0.4%
5 15
 
1.1%
4 37
 
2.7%
3 83
 
6.0%
2 204
 
14.7%
1 482
34.6%
0 561
40.3%

Armor Cache
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8060345
Minimum0
Maximum15
Zeros584
Zeros (%)42.0%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:51.243963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile8
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7110591
Coefficient of variation (CV)1.5011115
Kurtosis5.0211013
Mean1.8060345
Median Absolute Deviation (MAD)1
Skewness2.1963061
Sum2514
Variance7.3498413
MonotonicityNot monotonic
2025-07-22T12:15:51.353000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 584
42.0%
1 346
24.9%
2 140
 
10.1%
3 88
 
6.3%
4 49
 
3.5%
5 47
 
3.4%
7 29
 
2.1%
6 28
 
2.0%
8 20
 
1.4%
9 18
 
1.3%
Other values (6) 43
 
3.1%
ValueCountFrequency (%)
0 584
42.0%
1 346
24.9%
2 140
 
10.1%
3 88
 
6.3%
4 49
 
3.5%
5 47
 
3.4%
6 28
 
2.0%
7 29
 
2.1%
8 20
 
1.4%
9 18
 
1.3%
ValueCountFrequency (%)
15 5
 
0.4%
14 3
 
0.2%
13 3
 
0.2%
12 8
 
0.6%
11 8
 
0.6%
10 16
1.1%
9 18
1.3%
8 20
1.4%
7 29
2.1%
6 28
2.0%

Melee Cache
Real number (ℝ)

High correlation  Zeros 

Distinct13
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4195402
Minimum0
Maximum12
Zeros543
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:51.456817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9280719
Coefficient of variation (CV)1.3582369
Kurtosis5.9631525
Mean1.4195402
Median Absolute Deviation (MAD)1
Skewness2.2229342
Sum1976
Variance3.7174612
MonotonicityNot monotonic
2025-07-22T12:15:51.573369image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 543
39.0%
1 428
30.7%
2 164
 
11.8%
3 97
 
7.0%
4 52
 
3.7%
5 37
 
2.7%
6 27
 
1.9%
7 17
 
1.2%
8 9
 
0.6%
9 6
 
0.4%
Other values (3) 12
 
0.9%
ValueCountFrequency (%)
0 543
39.0%
1 428
30.7%
2 164
 
11.8%
3 97
 
7.0%
4 52
 
3.7%
5 37
 
2.7%
6 27
 
1.9%
7 17
 
1.2%
8 9
 
0.6%
9 6
 
0.4%
ValueCountFrequency (%)
12 3
 
0.2%
11 3
 
0.2%
10 6
 
0.4%
9 6
 
0.4%
8 9
 
0.6%
7 17
 
1.2%
6 27
 
1.9%
5 37
 
2.7%
4 52
3.7%
3 97
7.0%

Medium Arms Cache
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7385057
Minimum0
Maximum18
Zeros546
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:51.680645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum18
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4805231
Coefficient of variation (CV)1.4268133
Kurtosis6.2101032
Mean1.7385057
Median Absolute Deviation (MAD)1
Skewness2.2886559
Sum2420
Variance6.152995
MonotonicityNot monotonic
2025-07-22T12:15:51.805177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 546
39.2%
1 365
26.2%
2 160
 
11.5%
3 97
 
7.0%
4 62
 
4.5%
5 47
 
3.4%
6 35
 
2.5%
7 20
 
1.4%
9 15
 
1.1%
8 14
 
1.0%
Other values (7) 31
 
2.2%
ValueCountFrequency (%)
0 546
39.2%
1 365
26.2%
2 160
 
11.5%
3 97
 
7.0%
4 62
 
4.5%
5 47
 
3.4%
6 35
 
2.5%
7 20
 
1.4%
8 14
 
1.0%
9 15
 
1.1%
ValueCountFrequency (%)
18 1
 
0.1%
15 1
 
0.1%
14 3
 
0.2%
13 2
 
0.1%
12 6
 
0.4%
11 11
0.8%
10 7
 
0.5%
9 15
1.1%
8 14
1.0%
7 20
1.4%

Heavy Arms Cache
Categorical

Imbalance 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size70.8 KiB
0.0
1213 
1.0
156 
2.0
 
19
3.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4176
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1213
87.1%
1.0 156
 
11.2%
2.0 19
 
1.4%
3.0 4
 
0.3%

Length

2025-07-22T12:15:52.091796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-22T12:15:52.198302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1213
87.1%
1.0 156
 
11.2%
2.0 19
 
1.4%
3.0 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 2605
62.4%
. 1392
33.3%
1 156
 
3.7%
2 19
 
0.5%
3 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2605
62.4%
. 1392
33.3%
1 156
 
3.7%
2 19
 
0.5%
3 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2605
62.4%
. 1392
33.3%
1 156
 
3.7%
2 19
 
0.5%
3 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2605
62.4%
. 1392
33.3%
1 156
 
3.7%
2 19
 
0.5%
3 4
 
0.1%

forfeit
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
False
1386 
True
 
6
ValueCountFrequency (%)
False 1386
99.6%
True 6
 
0.4%
2025-07-22T12:15:52.301880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

score_pct_of_war_total
Real number (ℝ)

High correlation 

Distinct676
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5
Minimum0
Maximum1
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:52.457097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.09855
Q10.25375
median0.5
Q30.74625
95-th percentile0.90145
Maximum1
Range1
Interquartile range (IQR)0.4925

Descriptive statistics

Standard deviation0.27352683
Coefficient of variation (CV)0.54705365
Kurtosis-1.4089017
Mean0.5
Median Absolute Deviation (MAD)0.2465
Skewness0
Sum696
Variance0.074816925
MonotonicityNot monotonic
2025-07-22T12:15:52.587704image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.35 8
 
0.6%
0.65 8
 
0.6%
0.286 7
 
0.5%
0.714 7
 
0.5%
0.748 7
 
0.5%
0.252 7
 
0.5%
0.723 6
 
0.4%
0.277 6
 
0.4%
0.24 6
 
0.4%
0.76 6
 
0.4%
Other values (666) 1324
95.1%
ValueCountFrequency (%)
0 2
0.1%
0.001 1
0.1%
0.003 2
0.1%
0.004 2
0.1%
0.006 1
0.1%
0.008 1
0.1%
0.013 1
0.1%
0.014 1
0.1%
0.017 1
0.1%
0.021 1
0.1%
ValueCountFrequency (%)
1 2
0.1%
0.999 1
0.1%
0.997 2
0.1%
0.996 2
0.1%
0.994 1
0.1%
0.992 1
0.1%
0.987 1
0.1%
0.986 1
0.1%
0.983 1
0.1%
0.979 1
0.1%

faction_total_wins
Categorical

High correlation 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
1
635 
0
374 
2
212 
3
132 
4
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1392
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
1 635
45.6%
0 374
26.9%
2 212
 
15.2%
3 132
 
9.5%
4 39
 
2.8%

Length

2025-07-22T12:15:52.739401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-22T12:15:52.868235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 635
45.6%
0 374
26.9%
2 212
 
15.2%
3 132
 
9.5%
4 39
 
2.8%

Most occurring characters

ValueCountFrequency (%)
1 635
45.6%
0 374
26.9%
2 212
 
15.2%
3 132
 
9.5%
4 39
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 635
45.6%
0 374
26.9%
2 212
 
15.2%
3 132
 
9.5%
4 39
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 635
45.6%
0 374
26.9%
2 212
 
15.2%
3 132
 
9.5%
4 39
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 635
45.6%
0 374
26.9%
2 212
 
15.2%
3 132
 
9.5%
4 39
 
2.8%

faction_total_wars
Real number (ℝ)

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3146552
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:52.995747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3266556
Coefficient of variation (CV)0.57315475
Kurtosis0.45350433
Mean2.3146552
Median Absolute Deviation (MAD)1
Skewness0.9705261
Sum3222
Variance1.7600151
MonotonicityNot monotonic
2025-07-22T12:15:53.099939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 480
34.5%
2 396
28.4%
3 255
18.3%
4 164
 
11.8%
5 60
 
4.3%
6 30
 
2.2%
7 7
 
0.5%
ValueCountFrequency (%)
1 480
34.5%
2 396
28.4%
3 255
18.3%
4 164
 
11.8%
5 60
 
4.3%
6 30
 
2.2%
7 7
 
0.5%
ValueCountFrequency (%)
7 7
 
0.5%
6 30
 
2.2%
5 60
 
4.3%
4 164
 
11.8%
3 255
18.3%
2 396
28.4%
1 480
34.5%

faction_win_percentage
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5
Minimum0
Maximum1
Zeros374
Zeros (%)26.9%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:53.231000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.38263191
Coefficient of variation (CV)0.76526381
Kurtosis-1.3813306
Mean0.5
Median Absolute Deviation (MAD)0.5
Skewness0.016632636
Sum696
Variance0.14640718
MonotonicityNot monotonic
2025-07-22T12:15:53.353544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 387
27.8%
0 374
26.9%
0.5 282
20.3%
0.3333333333 102
 
7.3%
0.6666666667 84
 
6.0%
0.25 56
 
4.0%
0.75 40
 
2.9%
0.4 20
 
1.4%
0.6 20
 
1.4%
0.8 10
 
0.7%
Other values (2) 17
 
1.2%
ValueCountFrequency (%)
0 374
26.9%
0.2 10
 
0.7%
0.25 56
 
4.0%
0.3333333333 102
 
7.3%
0.4 20
 
1.4%
0.5 282
20.3%
0.5714285714 7
 
0.5%
0.6 20
 
1.4%
0.6666666667 84
 
6.0%
0.75 40
 
2.9%
ValueCountFrequency (%)
1 387
27.8%
0.8 10
 
0.7%
0.75 40
 
2.9%
0.6666666667 84
 
6.0%
0.6 20
 
1.4%
0.5714285714 7
 
0.5%
0.5 282
20.3%
0.4 20
 
1.4%
0.3333333333 102
 
7.3%
0.25 56
 
4.0%

rank_number_before
Real number (ℝ)

High correlation  Zeros 

Distinct21
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.704023
Minimum0
Maximum20
Zeros77
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:53.491300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q314
95-th percentile19
Maximum20
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.9712983
Coefficient of variation (CV)0.6860389
Kurtosis-1.21967
Mean8.704023
Median Absolute Deviation (MAD)6
Skewness0.18312666
Sum12116
Variance35.656404
MonotonicityNot monotonic
2025-07-22T12:15:53.633937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 117
 
8.4%
1 114
 
8.2%
14 95
 
6.8%
6 95
 
6.8%
15 92
 
6.6%
16 79
 
5.7%
0 77
 
5.5%
3 73
 
5.2%
10 69
 
5.0%
7 68
 
4.9%
Other values (11) 513
36.9%
ValueCountFrequency (%)
0 77
5.5%
1 114
8.2%
2 117
8.4%
3 73
5.2%
4 67
4.8%
5 43
 
3.1%
6 95
6.8%
7 68
4.9%
8 64
4.6%
9 52
3.7%
ValueCountFrequency (%)
20 43
3.1%
19 32
 
2.3%
18 41
2.9%
17 23
 
1.7%
16 79
5.7%
15 92
6.6%
14 95
6.8%
13 51
3.7%
12 49
3.5%
11 48
3.4%

rank_number_after
Real number (ℝ)

High correlation  Zeros 

Distinct21
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8678161
Minimum0
Maximum20
Zeros45
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:53.813165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median8
Q314
95-th percentile19
Maximum20
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.8613061
Coefficient of variation (CV)0.66096387
Kurtosis-1.2089068
Mean8.8678161
Median Absolute Deviation (MAD)5
Skewness0.18354765
Sum12344
Variance34.354909
MonotonicityNot monotonic
2025-07-22T12:15:53.985238image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
14 112
 
8.0%
1 111
 
8.0%
2 111
 
8.0%
3 100
 
7.2%
6 92
 
6.6%
10 88
 
6.3%
16 87
 
6.2%
15 74
 
5.3%
7 70
 
5.0%
8 65
 
4.7%
Other values (11) 482
34.6%
ValueCountFrequency (%)
0 45
3.2%
1 111
8.0%
2 111
8.0%
3 100
7.2%
4 63
4.5%
5 52
3.7%
6 92
6.6%
7 70
5.0%
8 65
4.7%
9 32
 
2.3%
ValueCountFrequency (%)
20 38
 
2.7%
19 44
 
3.2%
18 37
 
2.7%
17 19
 
1.4%
16 87
6.2%
15 74
5.3%
14 112
8.0%
13 50
3.6%
12 42
 
3.0%
11 60
4.3%

total_cache_value
Real number (ℝ)

High correlation  Zeros 

Distinct507
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1246.0453
Minimum0
Maximum8267
Zeros74
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2025-07-22T12:15:54.148753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1309
median610
Q31449.5
95-th percentile4934.8
Maximum8267
Range8267
Interquartile range (IQR)1140.5

Descriptive statistics

Standard deviation1574.2166
Coefficient of variation (CV)1.2633703
Kurtosis4.5276304
Mean1246.0453
Median Absolute Deviation (MAD)420
Skewness2.1486651
Sum1734495
Variance2478157.9
MonotonicityNot monotonic
2025-07-22T12:15:54.308435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
 
5.3%
133 70
 
5.0%
111 60
 
4.3%
420 51
 
3.7%
190 47
 
3.4%
323 42
 
3.0%
309 39
 
2.8%
442 30
 
2.2%
301 27
 
1.9%
244 27
 
1.9%
Other values (497) 925
66.5%
ValueCountFrequency (%)
0 74
5.3%
111 60
4.3%
133 70
5.0%
190 47
3.4%
222 17
 
1.2%
244 27
 
1.9%
266 16
 
1.1%
301 27
 
1.9%
309 39
2.8%
323 42
3.0%
ValueCountFrequency (%)
8267 1
0.1%
8205 1
0.1%
8117 1
0.1%
8041 1
0.1%
8015 1
0.1%
8012 1
0.1%
7976 1
0.1%
7908 1
0.1%
7885 1
0.1%
7686 1
0.1%

Interactions

2025-07-22T12:15:41.870142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:50.105037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:53.404807image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:56.823101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:59.166108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:01.638835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:04.082910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:06.400541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:08.530111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:10.570029image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:12.678190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:15.193812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:17.484919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:20.149032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:23.137762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:25.676480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:28.360558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:30.833412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:33.605598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:36.402518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:38.794021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:41.967046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:50.282857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:53.524513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:56.934932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:59.275000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:01.741673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:04.197742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:06.502068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:08.617096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:10.661680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:12.778711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:15.323902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:17.572859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:20.347128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:23.245539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:25.789033image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:28.476546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:30.940002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:33.799704image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:36.510404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:38.906554image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:42.073703image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:50.511727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:53.656069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:57.054732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:59.387868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:01.854222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:04.316823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:06.602907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:08.723160image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:10.765370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:12.893230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:15.437442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:17.671401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:20.538895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:23.375517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:25.932493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:28.597866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:31.093481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:33.974249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:36.622154image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:39.055802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:42.168239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:50.739646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:53.777287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:57.156718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:59.509248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-22T12:15:32.300861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:35.266672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:37.762717image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:40.753978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:43.182747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:52.268783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:55.327142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:58.197535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:00.584591image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:03.121767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:05.467138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:07.700560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:09.704302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:11.902480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:14.033146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:16.641849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:18.744707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:22.150141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:24.775635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:27.262866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:29.927395image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:32.422684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:35.514638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:37.869782image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:40.886839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:43.276045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:52.403922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:55.537077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:58.293405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:00.694115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:03.230204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:05.573197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:07.798084image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:09.791822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:11.989331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:14.157218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:16.750859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:18.874948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:22.274055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:24.874280image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:27.392735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:30.029590image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:32.536544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:35.619055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:37.974685image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:41.006968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:43.383440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:52.560970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:55.748765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:58.416048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:00.807660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:03.351139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:05.676391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:07.900612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:09.893204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:12.097264image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:14.406457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:16.875054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:19.197570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:22.397968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:24.996026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:27.526146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:30.148137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:32.735132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:35.730842image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:38.091550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:41.155348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:43.485360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:52.712622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:55.958436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:58.520582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:00.917787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:03.463941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:05.776916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:08.001131image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:09.982837image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:12.194335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:14.520531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:16.976804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:19.335035image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:22.519794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:25.105730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:27.684729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:30.255030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:32.861678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:35.840641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:38.210056image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:41.295113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:43.586183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:52.863247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:56.273037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:58.618737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:01.193240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:03.575644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:06.000212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:08.095663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:10.199314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:12.285176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:14.653949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:17.076184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:19.476948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:22.652327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:25.209638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:27.827140image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:30.364907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:32.980231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:35.948446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:38.325673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:41.420427image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:43.713307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:52.988346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:56.407355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:58.730156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:01.294846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:03.705803image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:06.100744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:08.196277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:10.284833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:12.378796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:14.778841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:17.174821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:19.622202image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:22.765758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:25.308273image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:27.976546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:30.471940image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:33.097208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:36.053741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:38.454211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:41.530971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:43.819841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:53.117958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:56.549191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:58.850678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:01.413450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:03.839805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:06.205783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:08.324323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:10.384729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:12.484579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:14.929291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:17.280639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:19.786576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:22.895137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:25.426122image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:28.103570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:30.592253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:33.243766image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:36.183651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:38.569906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:41.651613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:43.936120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:53.253558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:56.698483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:14:59.054559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:01.536330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:03.974653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:06.309313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:08.435049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:10.482552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:12.588671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:15.075266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:17.394219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:19.969595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:23.019802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:25.558985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:28.242538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:30.718073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:33.388097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:36.297429image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:38.690488image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T12:15:41.764145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-07-22T12:15:54.488166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Armor CacheHeavy Arms CacheMedium Arms CacheMelee CacheSmall Arms Cacheattacksduration_hoursduration_minutesfaction_idfaction_total_warsfaction_total_winsfaction_win_percentageforfeitmember_countparticipating_member_countrank_afterrank_beforerank_number_afterrank_number_beforereward_pointsreward_respectscorescore_pct_of_war_totaltotal_cache_valuetotal_war_scorewar_idwon
Armor Cache1.0000.4160.5620.5020.3630.5690.0700.070-0.5930.2050.1760.3020.0000.6210.6580.3650.2960.7400.6800.5190.8460.7270.3880.8670.6050.0210.321
Heavy Arms Cache0.4161.0000.2820.2560.1790.1430.0810.0810.1930.1160.1220.0860.0000.2420.2640.2690.2590.2500.2380.2680.3580.1960.1020.3830.1610.0000.161
Medium Arms Cache0.5620.2821.0000.5110.3680.5330.0840.084-0.5770.2070.1830.2970.0000.5960.6320.3370.2880.7080.6550.4920.8110.6800.3790.7850.5720.0280.304
Melee Cache0.5020.2560.5111.0000.2980.4430.1030.103-0.5310.1900.1740.2250.0000.5290.5450.2770.2470.5950.5610.4440.6800.5630.2700.6620.514-0.0330.241
Small Arms Cache0.3630.1790.3680.2981.0000.3110.0560.056-0.3360.1590.1630.1820.0000.3530.3830.1980.1790.4320.4010.3000.4850.4110.2420.5010.3360.0010.216
attacks0.5690.1430.5330.4430.3111.000-0.023-0.023-0.3290.1090.1510.4810.0000.4150.6730.1880.1300.3940.2790.3650.6850.9220.6840.6830.576-0.0550.459
duration_hours0.0700.0810.0840.1030.056-0.0231.0001.0000.0380.0470.0400.0180.071-0.160-0.1210.1300.1520.0590.057-0.0250.096-0.0340.0000.096-0.1480.0280.000
duration_minutes0.0700.0810.0840.1030.056-0.0231.0001.0000.0380.0470.0400.0180.071-0.160-0.1210.1300.1520.0590.057-0.0250.096-0.0340.0000.096-0.1480.0280.000
faction_id-0.5930.193-0.577-0.531-0.336-0.3290.0380.0381.000-0.2330.191-0.0510.017-0.695-0.6250.2880.286-0.768-0.771-0.577-0.686-0.510-0.032-0.682-0.6950.0940.000
faction_total_wars0.2050.1160.2070.1900.1590.1090.0470.047-0.2331.0000.4690.0050.0000.2760.2400.1630.1680.2700.2840.2260.2430.1730.0040.2430.239-0.0390.038
faction_total_wins0.1760.1220.1830.1740.1630.1510.0400.0400.1910.4691.0000.6610.0000.1090.1790.2770.1880.2090.1760.1890.2100.1440.3090.2120.0560.0610.615
faction_win_percentage0.3020.0860.2970.2250.1820.4810.0180.018-0.0510.0050.6611.0000.0000.0200.1880.2070.1150.2060.0660.1380.3910.4440.6700.3890.0130.0030.761
forfeit0.0000.0000.0000.0000.0000.0000.0710.0710.0170.0000.0000.0001.0000.0620.0470.1220.1370.0650.0910.0630.0470.0000.1750.0340.0000.0610.000
member_count0.6210.2420.5960.5290.3530.415-0.160-0.160-0.6950.2760.1090.0200.0621.0000.8280.2870.2930.6390.6460.4730.6860.5980.0130.6820.8150.0090.000
participating_member_count0.6580.2640.6320.5450.3830.673-0.121-0.121-0.6250.2400.1790.1880.0470.8281.0000.2820.2450.6060.5720.4750.7640.7680.2880.7610.7850.0080.285
rank_after0.3650.2690.3370.2770.1980.1880.1300.1300.2880.1630.2770.2070.1220.2870.2821.0000.6630.9960.7250.3970.4750.2260.2140.4670.2210.0000.559
rank_before0.2960.2590.2880.2470.1790.1300.1520.1520.2860.1680.1880.1150.1370.2930.2450.6631.0000.7090.9960.3230.3770.1950.0930.3780.2550.0620.040
rank_number_after0.7400.2500.7080.5950.4320.3940.0590.059-0.7680.2700.2090.2060.0650.6390.6060.9960.7091.0000.9780.6160.8510.6150.1770.8490.716-0.0060.321
rank_number_before0.6800.2380.6550.5610.4010.2790.0570.057-0.7710.2840.1760.0660.0910.6460.5720.7250.9960.9781.0000.5980.7770.5170.0200.7740.726-0.0090.000
reward_points0.5190.2680.4920.4440.3000.365-0.025-0.025-0.5770.2260.1890.1380.0630.4730.4750.3970.3230.6160.5981.0000.6090.5070.1590.6010.530-0.4290.273
reward_respect0.8460.3580.8110.6800.4850.6850.0960.096-0.6860.2430.2100.3910.0470.6860.7640.4750.3770.8510.7770.6091.0000.8580.4880.9960.699-0.0070.341
score0.7270.1960.6800.5630.4110.922-0.034-0.034-0.5100.1730.1440.4440.0000.5980.7680.2260.1950.6150.5170.5070.8581.0000.6100.8550.735-0.0390.369
score_pct_of_war_total0.3880.1020.3790.2700.2420.6840.0000.000-0.0320.0040.3090.6700.1750.0130.2880.2140.0930.1770.0200.1590.4880.6101.0000.4880.0000.0000.997
total_cache_value0.8670.3830.7850.6620.5010.6830.0960.096-0.6820.2430.2120.3890.0340.6820.7610.4670.3780.8490.7740.6010.9960.8550.4881.0000.6940.0010.357
total_war_score0.6050.1610.5720.5140.3360.576-0.148-0.148-0.6950.2390.0560.0130.0000.8150.7850.2210.2550.7160.7260.5300.6990.7350.0000.6941.000-0.0380.000
war_id0.0210.0000.028-0.0330.001-0.0550.0280.0280.094-0.0390.0610.0030.0610.0090.0080.0000.062-0.006-0.009-0.429-0.007-0.0390.0000.001-0.0381.0000.000
won0.3210.1610.3040.2410.2160.4590.0000.0000.0000.0380.6150.7610.0000.0000.2850.5590.0400.3210.0000.2730.3410.3690.9970.3570.0000.0001.000

Missing values

2025-07-22T12:15:44.224715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-22T12:15:44.679789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

start_dateend_dateduration_hoursduration_minuteswar_idfaction_idfaction_namewonscoretotal_war_scoreattacksrank_beforerank_aftermember_countparticipating_member_countreward_respectreward_pointsSmall Arms CacheArmor CacheMelee CacheMedium Arms CacheHeavy Arms Cacheforfeitscore_pct_of_war_totalfaction_total_winsfaction_total_warsfaction_win_percentagerank_number_beforerank_number_aftertotal_cache_value
02025-07-11 14:00:002025-07-15 02:00:5784.025041.02801333241Nub NavyFalse381614668459Diamond IDiamond97461074402.04.03.03.01.0False0.260020.00000018172797.0
12025-07-11 14:00:002025-07-15 02:00:5784.025041.02801341929ValorTrue1085214668796Diamond IDiamond II100272552005.011.02.011.01.0False0.740230.66666718196680.0
22024-11-08 20:00:002024-11-11 23:00:5575.024501.01907550356Colombo Crime FamilyFalse27537050614Silver ISilver1912144300.00.02.00.00.0False0.390010.00000065266.0
32024-11-08 20:00:002024-11-11 23:00:5575.024501.01907550568Toxic SpawnTrue42977050827Silver ISilver II151122937570.01.00.01.00.0False0.610221.00000067499.0
42025-02-01 18:00:002025-02-03 11:35:0941.592495.02179441777The SwarmFalse610417792Gold IIGold I11876302.00.00.00.00.0False0.146010.0000001110222.0
52025-02-01 18:00:002025-02-03 11:35:0941.592495.02179447600Valhalla WarriorsTrue35674177615Gold IGold II1311311710292.00.02.01.00.0False0.854221.0000001011678.0
62025-06-28 20:00:002025-07-01 01:18:1553.303198.02726051708Tribunal Global NetworkFalse406111203360Silver IISilver I3623241900.02.00.00.00.0False0.362010.00000076618.0
72025-06-28 20:00:002025-07-01 01:18:1553.303198.02726052787BlissTrue7142112031064Silver ISilver II2523337702.01.01.01.00.0False0.638221.00000067854.0
82024-11-16 22:00:002024-11-19 04:56:0654.943296.01927650642midnight clawTrue45377278783Bronze IIBronze III2319252802.01.01.00.00.0False0.623340.75000034664.0
92024-11-16 22:00:002024-11-19 04:56:0654.943296.01927650723ShaggersFalse27417278656Bronze IBronze2921114001.00.01.00.00.0False0.377140.25000021244.0
start_dateend_dateduration_hoursduration_minuteswar_idfaction_idfaction_namewonscoretotal_war_scoreattacksrank_beforerank_aftermember_countparticipating_member_countreward_respectreward_pointsSmall Arms CacheArmor CacheMelee CacheMedium Arms CacheHeavy Arms Cacheforfeitscore_pct_of_war_totalfaction_total_winsfaction_total_warsfaction_win_percentagerank_number_beforerank_number_aftertotal_cache_value
13822024-11-08 17:00:002024-11-10 03:00:2834.012040.01905147445Blade of VengeanceFalse9455409150Silver IISilver I20108442790.00.01.00.00.0False0.175120.50000076133.0
13832024-11-08 17:00:002024-11-10 03:00:2834.012040.01905149147GladiatorsTrue44645409672Silver IIIGold I2922374412362.01.00.02.00.0False0.825120.500000810911.0
13842024-12-27 20:00:002024-12-29 13:49:3041.832510.02055551754Outlaws HQ IITrue19082334483Bronze IIBronze III1311203701.00.01.01.00.0False0.817450.80000034434.0
13852024-12-27 20:00:002024-12-29 13:49:3041.832510.02055552793Crimson TechFalse426233486Bronze IBronze13847201.00.00.00.00.0False0.183020.00000021111.0
13862025-04-12 19:00:002025-04-13 11:01:2516.02961.02416617924The Brotherhood of LightFalse12207940186Bronze IIIBronze II45197172370.00.01.00.00.0False0.154010.00000043133.0
13872025-04-12 19:00:002025-04-13 11:01:2516.02961.02416620211CR-ACKTrue67207940955SilverSilver I5140502116571.02.01.02.00.0False0.846150.200000561242.0
13882025-04-26 20:00:002025-04-27 21:12:3325.211513.02460946529Human InstinctTrue51726423832Gold IGold II1715383700.02.01.01.00.0False0.805111.0000001011941.0
13892025-04-26 20:00:002025-04-27 21:12:3325.211513.02460949247DirtyDogMilitantsFalse12516423137Gold IIGold I16512083990.01.00.00.00.0False0.195460.6666671110309.0
13902024-12-14 18:00:002024-12-17 22:11:2476.194571.02014950711Diamond CartelFalse23025546625Bronze IIBronze I1311101801.00.01.00.00.0False0.415010.00000032244.0
13912024-12-14 18:00:002024-12-17 22:11:2476.194571.02014952484Valors EdgeTrue32445546495Bronze IIISilver I164237001.01.01.00.00.0False0.585111.00000046553.0